{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pylab\n",
    "from sklearn.linear_model import LinearRegression\n",
    "plt.rcParams['figure.figsize'] = (15.0,5.0) # 设置figure_size尺寸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# 生成x数据\n",
    "x=np.linspace(0,10,20)\n",
    "y_net=np.power(x,2)+3  # 生成没有噪音的目标值y_net=x^2+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise=4*np.random.randn(20)  # 生成服从正态分布的噪音数据，标准差为4，期望为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20,) \n",
      " [3.02897574 0.13609657 0.81780177 1.11184983 6.64193407]\n"
     ]
    }
   ],
   "source": [
    "y=y_net+noise\n",
    "print(y.shape,'\\n',y[:5])  # 对y值进行噪音上的叠加，并查看部分数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对x进行形状上的改变，sklearn要求样本输入满足（n,d）的形式要求，n为样本数量，d为特征数量\n",
    "X=x[:,np.newaxis]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用1维特征对数据点进行拟合\n",
    "lr=LinearRegression().fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12399470>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,lr.predict(X))  # 绘制拟合的曲线\n",
    "plt.plot(x,y,'o')          # 绘制样本点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.43696565, 0.83215965])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX=np.tile(X,2)            # 对X的维度进行拓展，拓展后的X的数据形状为（n,2）\n",
    "XX[:,1]=np.power(XX[:,1],2) # 第2列数据为x^2\n",
    "lr2=LinearRegression().fit(XX,y) #使用拓展的特征进行拟合\n",
    "lr2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1452a160>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,lr2.predict(XX)) # 绘制拟合后的曲线\n",
    "plt.plot(x,y,'o')           # 绘制样本点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14747b70>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=plt.subplot(121)\n",
    "ax2=plt.subplot(122)\n",
    "ax1.plot(x,lr.predict(X))\n",
    "ax1.plot(x,y,'o')\n",
    "ax2.plot(x,lr2.predict(XX))\n",
    "ax2.plot(x,y,'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
